The last problem of discrete graph structure learning is that the sampling operation from \(\mathbf {\Theta }\)  to adjacent matrix \(\mathbf {A}\)  is not differentiable.
Hence, we apply the Gumbel-Softmax reparametrization trick proposed by [1]}, [2]}:
\(\mathbf {A}_{ij}=\text{softmax}((\mathbf {\Theta }_{ij}+\mathbf {g})/\tau ),\) 
